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Phin Security | MSP Focused Phishing Training & Simulation — 18 websites ranked by shared content topics, category and on-page relevance.

Each result shows its full tech stack, contacts and AI-policy — not just a name · Browse all sites in Information And Network Security →

Domain	Match	Title	Country/Lang	Category	AI files	Contact	AI-protection
0penai-gpt.com ↗	75 match 1 shared topics	Phishing Simulation Landing Page	en	information-and-network-security	robotsllmsaihumans	emailphone	none
2fanotifications.com ↗	75 match 1 shared topics	Phishing Simulation Landing Page	en	information-and-network-security	robotsllmsaihumans	emailphone	none
1f.io ↗	72 match 1 shared topics	Phishing Simulation & Awareness Training - Fished	en	information-and-network-security	robotsllmsaihumans	emailphone	none
baitcheck.com ↗	71 match 1 shared topics	BaitCheck - Security Awareness Training	en	information-and-network-security	robotsllmsaihumans	emailphone	none
cybercollaborationcenter.org ↗	70 match 1 shared topics	Cyber Collaboration Center – Cybersecurity Training and Education	en	information-and-network-security WordPress WooCommerce	robotsllmsaihumans	emailphone	none
cs-academy.org ↗	70 match 1 shared topics	CS-Academy — Cybersecurity, Ethical Hacking & GNU/Linux Training	en	information-and-network-security	robotsllmsaihumans	emailphone	none
cyberbattle.tech ↗	70 match 1 shared topics	Cyber Battle - Interactive Cybersecurity Training Game	en	information-and-network-security	robotsllmsaihumans	emailphone	none
inboxshieldpro.com ↗	69 match 1 shared topics	AI Email Security \| Phishing Protection \| Inbox Shield Pro	en	information-and-network-security	robotsllmsaihumans	emailphone	none
in7ruder.com ↗	69 match 1 shared topics	Security-driven Web Apps, Training & Pentesting	en	information-and-network-security	robotsllmsaihumans	emailphone	none
impalabs.com ↗	69 match 1 shared topics	Impalabs - Security Audits, Consulting and Training	en	information-and-network-security	robotsllmsaihumans	emailphone	none
csrmc.org ↗	69 match 1 shared topics	BAI Information Security: Risk Management Framework Training	en	information-and-network-security WordPress	robots llmsaihumans	emailphone	none
cybersafelit.sbs ↗	69 match 1 shared topics	Cyber Safe Lit — Cybersecurity Training in 京都市	en	information-and-network-security	robotsllmsaihumans	email phone	none
cybersecure.wales ↗	69 match 1 shared topics	Cyber Security Training	en	information-and-network-security	robotsllmsaihumans	emailphone	none
learn-bits.org ↗	69 match 1 shared topics	LearnBits - Cybersecurity Training Platform	en	information-and-network-security	robotsllmsaihumans	emailphone	none
cm-alliance.com ↗	68 match 1 shared topics	Cyber Security Training and Cybersecurity Consulting UK	en	information-and-network-security HubSpot	robotsllmsaihumans	emailphone	none
cybermgtalliance.one ↗	68 match 1 shared topics	Cyber Security Training and Cybersecurity Consulting UK	en	information-and-network-security HubSpot	robotsllmsaihumans	emailphone	none
cyber-mgmt-alliance.sbs ↗	68 match 1 shared topics	Cyber Security Training and Cybersecurity Consulting UK	en	information-and-network-security HubSpot	robotsllmsaihumans	emailphone	none
cyber-mgmt-alliance.business ↗	68 match 1 shared topics	Cyber Security Training and Cybersecurity Consulting UK	en	information-and-network-security HubSpot	robotsllmsaihumans	emailphone	none

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

shared topics how many of the same fine‑grained content topics (IAB sub‑categories) both sites cover — the main signal.
same category whether they sit in the same business / industry category.
content & title how closely their titles and page descriptions line up in wording and meaning.

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.